Deep Convolutional Neural Networks for Automated Butterfly Species Recognition and Classification

In this research paper, we propose the use of Deep Convolutional Neural Networks (CNNs) a deep learning (DL) technique for accurate and efficient recognition and classification of butterfly species based on their visual features and patterns. Automated butterfly species recognition and classification play a vital role in biodiversity studies, ecological monitoring, and conservation efforts. The biodiversity of any region relies on habitability of that region for various species. Understanding of geographical distribution of rare and endangered species like butterflies and others can be considered as an important aspect for ensuring sustainable conservation of ecology. They can be useful in predicting climatic condition changes based on the regions supporting their habitability. Also tracking the migration pattern can be useful information for supporting ecological development. This study is focused specifically to automate the classification of butterfly species like Tigers and Emigrants category, since they are more prevalent in the Indian region. This study can be considered a probabilistic data driven approach, as we have curated imaging dataset specific only to the Indian regions. This study used techniques like web scrapping to gather and curate imaging dataset. In this study application of CNN based method is analyzed. The overall performance of CNN is approximately 88% which is comparable to other automatic classification techniques reported in literature. Thus, it can be concluded that application of deep CNNs for automated butterfly species recognition offers promising prospects for efficient and accurate identification in biodiversity research. By automating the identification process, this technology can streamline data collection and analysis, supporting ecological studies, conservation efforts, and the understanding of butterfly populations, their migration pattern, their habitats, and even new species evolution.